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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 1101–1125 of 9051 papers

TitleStatusHype
Motion Modes: What Could Happen Next?—0
Improving the performance of weak supervision searches using data augmentation—0
Clinical Document Corpora and Assorted Domain Proxies: A Survey of Diversity in Corpus Design, with Focus on German Text Data—0
On Domain-Specific Post-Training for Multimodal Large Language Models—0
T2Vid: Translating Long Text into Multi-Image is the Catalyst for Video-LLMsCode1
The role of inhibitory neuronal variability in modulating phase diversity between coupled networks—0
Retrieval-guided Cross-view Image Synthesis—0
DELT: A Simple Diversity-driven EarlyLate Training for Dataset DistillationCode1
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics—0
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
Global Tensor Motion PlanningCode1
Differential learning kinetics govern the transition from memorization to generalization during in-context learning—0
Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Diffusion Autoencoders for Few-shot Image Generation in Hyperbolic Space—0
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object ManipulationCode5
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric LearningCode0
Network Inversion and Its Applications—0
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation—0
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model—0
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